Coronary blood vessel illnesses are one of the main causes of death for people throughout the entire industrial world. A heart malfunction is in this case caused mainly by abnormal constrictions, that is to say stenoses, in the coronary arteries. This often leads to a life-threatening myocardial infarction.
Technical improvements in methods such as spiral CT (Multi-Slice Cardiac Computed Tomography—MSCT), have recently made it possible to produce three-dimensional images of the heart with high position resolution in the submillimeter range. In this case, 64 parallel slice displays of the interior of the patient, for example of the chest area with the heart, are recorded within a time window of about 20 seconds. In particular, this makes it possible to display coronary arteries by intravenous injection of a contrast agent. This technique is known, for example, as 3D angiography (Computed Tomography Angiography—CTA). An imaging method such as this results in a digital 3D image data record of the patient.
In order to allow this record to be evaluated to determine the state of the heart or of the coronary vessels of the patient, it is necessary to identify and to display with high contrast the so-called coronary tree from the image data. The coronary tree in this case represents the totality of the coronary arteries or vessels without the other image information contained in the image data record. In other words, the aim is to carry out an extraction process from the image data of the coronary tree in such a manner that this can be displayed or measured, for example on its own, so to speak in empty space. Such processing of the coronary tree from the image data, which is also referred to as segmentation in specialist circles, will always be referred to as identification in the following text.
The quality of this identification is critical for subsequent medical diagnosis, for example the evaluation of the presence or the degree of vessel stenoses. Vessel stenoses are created by fat deposits, calcium deposits, aneurisms or other anatomical abnormalities, such as the lack of a coronary vessel or the like.
In addition to the process of finding the coronary blood vessels, that is to say just the confirmation of a diagnosis, their qualitative and quantitative analysis is also very useful, for example for planning the treatment before a required action, such as the insertion of a stent. In this case, by way of example, it is possible to determine the length of the stent, the length of catheters that may be required or the size of a bypass, even prior to an operation.
The identification of corresponding blood vessels in medical image data represents a major requirement, because the structures of the blood vessels are often very thin. For example, the diameter of blood vessels often covers only a small number of pixels or voxels in the digital image data. A further difficulty in the identification process is the proximity of the blood vessels to be identified to ventricles or atria which are likewise displayed with high contrast in the image data. Measurement noise and image interference, furthermore, require robust methods and algorithms for appropriate image processing.
A large number of methods have therefore been developed for identification of blood vessels in medical 2D or 3D images and image data.
One class of methods is based on so-called neighborhood differences, which are calculated from the image data. The major disadvantage of these methods is their critical dependency on noise in the image data.
Furthermore, methods exist based on deformable models with or without shape originals. The aim of the most models and methods in this case is to identify tube-like structures, but they have problems or fail in the identification of vessel branches in the coronary tree. Furthermore, shape models require a labor-intensive training phase on a large representative database of known image data with manually segmented vessel or coronary trees. Abnormal vessel trees (which, however, represent the most difficult and most critical case in medical practice) are often incorrectly segmented since they differ too much from the average pattern or the average shape model. Although model-based methods are considerably more robust to image noise than those mentioned above, most of these methods are not suitable for 3D analysis. This is also due to the fact that they are highly computation intensive.
Line and contour algorithms, in contrast, extract or identify only the center line of a blood vessel in image data, but not the entire vessel volume or volumes. Furthermore, techniques such as these require very good manual initialization. They therefore require a considerable amount of preparatory work by a very highly experienced user, such as a vascular surgeon. By way of example, the setting of appropriate markers for initialization of a method such as this is highly complex in practice, particularly in the 3D field.
One very widely used and fundamental algorithm is the so-called region growing or growth process, which is in general widespread use for the segmentation of image data and is already in use for vessel identification and segmentation, for example on the basis of [Boskamp, Tobias et al., “New Vessel Analysis Tool for Morphometric Quantification and Visualization of vessels in CT and MR Imaging Data Sets”, RadioGraphics, Vol. 24, No. 1, pages 287-297, published online, 2005].
The method operates for 2D or 3D situations, and is in this case based on the brightness, color or gray-scale values of the voxels in the image data. The basic assumption of the method in this case is that the voxels which are located close to one another and have similar brightness values are very highly probably associated with the same object. Without any restriction to generality, the following text is based on the assumption that contrasted vessels, that is to say blood vessels which have been enhanced by contrast agent are displayed more brightly in the image data than surrounding tissue or the like, and that brighter voxels have higher gray-scale or intensity values. The 3D image data to be segmented comprises pure gray-scale images, that is to say not color images. The gray-scale values of the voxels are displayed in Hounsfield units.
It is generally first of all convenient to add a first seed point sp, that is to say a single image voxel, to an empty seed point set SPT. T is in this case the gray-scale value of the seed point. Starting from this, the method segments the image data and identifies the blood vessels by checking voxels nsp which are adjacent to the seed point sp step-by-step to determine whether they are potential vessel voxels. Vessel voxels such as these are added as potential seed points to the seed point sets SPI, on the basis of their gray-scale values I. As described below, correspondingly stored seed points are processed when T assumes the corresponding value I.
If all neighbours have been checked for a threshold value T, the seed point is itself added to an empty segmented region R. The region R represents the growing vessel tree.
An overall interval of value Tmax≧T≧Tmin is in general selected for corresponding thresholds or threshold values T, with the current threshold value T being the reduced step-by-step during the course of the method.
When no further adjacent voxels nsp can be found to the already segmented region R whose intensity is greater than the current threshold value T, this threshold value T is reduced by a predetermined step width, and a further method run is carried out with the modified threshold value T.
Seed points which are segmented, that is to say have been associated with the set R are in this case marked in the normal manner with the corresponding threshold value T at which they were identified. This creates a result R which is subdivided on the basis of intensity values and allows step-by-step removal of already segmented regions, even after processing of the entire algorithm.
The maximum threshold value Tmax is in this case the intensity of the first seed point, which was determined manually. The minimum threshold value for Tmin is defined interactively by the user. The segmentation is carried out over the entire threshold interval Tmin to Tmax.
The user can admittedly reduce the number of incorrect segmentations, for example of ventricles, by increasing the minimum threshold value Tmin. However, this has the disadvantage that all of the blood vessels which have been identified correctly but have a lower intensity than Tmin likewise disappear from the result. If the algorithm branches during the search process into areas such as the left-hand ventricle with a high threshold value, only a fraction of the vessels to be segmented, specifically that of the very bright vessels, can therefore be segmented, without reaching large areas of incorrect segmentations.
The growth algorithm which is known from the prior art will now be explained in detail with reference to FIG. 9.
First of all, in a start step 300, a user who is not shown manually selects a first voxel as the seed point sp in 3D image data, which is not shown. The gray-scale value I of this seed point sp is selected as the maximum threshold value Tmax. The seed point sp is then written as the only seed point to the set SPTmax. The user also defines the lower threshold value limit Tmin on the basis of his empirical values.
At the start of the loop 302a, the value range is first of all defined for the current threshold value T between the upper threshold value limit Tmax and the lower threshold value limit Tmin, in which case the current threshold value T initially assumes the upper threshold limit Tmax. The first seed point sp from the seed point set SPT is once again chosen at the start of the following loop 304a, and thus initially corresponds to the abovementioned set SPTmax. At the start of the following loop 306a, the first adjacent voxel nsp of the seed point sp to the seed point sp which has just been selected is once again selected from the set of adjacent voxels which are directly adjacent to sp.
In the branch 308, a check is now carried out for the adjacent voxel nsp to determine whether its brightness value or intensity value HU(nsp) is greater than or equal to the lower threshold value limit Tmin. If this criterion is satisfied, the adjacent voxel nsp is written as the new seed point to a seed point set SPI, or is added to it, in the step 311 in the YES branch 310. The threshold value I of the seed point set is in this case determined from the minimum intensity of the adjacent voxel HU(nsp) and the current threshold value T. In contrast, nothing further is done in the corresponding NO branch 312.
A check is then carried out at the end of the loop 306b to determine whether a further adjacent voxel nsp to the current seed point sp exists. If YES, this is selected and the algorithm jumps back to the start of the loop 306a, but if NO, the seed point sp is added to the result set R in step 314, with the seed point sp being marked with the current threshold value T. Furthermore, sp is removed from the seed point set SPT, and its processing is thus complete.
A check is then carried out at the end of the loop 304b to determine whether there are any further seed points sp in the seed point set SPT. If YES, a jump is made to the start of the loop 304a, and the next seed point is selected from the set SPT. If NO, a jump is made to the end of the loop 302b, in which the current threshold value T is reduced by a fixed step width, in the present example by one gray-level step, in the direction of Tmin, and a jump is then made to the start of the loop 302a. 
The algorithm ends after the last method run with the current threshold value T=Tmin.
The result set R now contains the segmented vessel tree, specifically all of the seed points sp which have been determined during the course of the growth algorithm, as well as the start seed point sp from the start step 300, with each seed point sp being associated with the current threshold value T at the time it was added to the result set R.
Although the regional growing method has the disadvantages mentioned above, its fundamental advantages are nevertheless the processing speed and the simplicity for implementation in a computer program.